**Michał Jasi ´nski 1,\* , Tomasz Sikorski <sup>1</sup> , Zbigniew Leonowicz <sup>1</sup> , Klaudiusz Borkowski <sup>2</sup> and El ˙zbieta Jasi ´nska <sup>3</sup>**


Received: 23 April 2020; Accepted: 9 May 2020; Published: 11 May 2020

**Abstract:** This article presents the application of data mining (DM) to long-term power quality (PQ) measurements. The Ward algorithm was selected as the cluster analysis (CA) technique to achieve an automatic division of the PQ measurement data. The measurements were conducted in an electrical power network (EPN) of the mining industry with distributed generation (DG). The obtained results indicate that the application of the Ward algorithm to PQ data assures the division with regards to the work of the distributed generation, and also to other important working conditions (e.g., reconfiguration or high harmonic pollution). The presented analysis is conducted for the area-related approach—all measurement point data are connected at an initial stage. The importance rate was proposed in order to indicate the parameters that have a high impact on the classification of the data. Another element of the article was the reduction of the size of the input database. The reduction of input data by 57% assured the classification with a 95% agreement when compared to the complete database classification.

**Keywords:** data mining; power quality; cluster analysis; ward algorithm; different working conditions; distributed generation
